MST-RNN: A Multi-Dimension Spatiotemporal Recurrent Neural Networks for Recommending the Next Point of Interest

Chunshan Li, Dongmei Li, Zhongya Zhang and Dianhui Chu
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Chunshan Li: School of Computer and Science, Harbin Institute of Technology, Weihai 264209, China
Dongmei Li: Network Center, Shanxi Medical University, Taiyuan 030607, China
Zhongya Zhang: School of Computer and Science, Harbin Institute of Technology, Weihai 264209, China
Dianhui Chu: School of Computer and Science, Harbin Institute of Technology, Weihai 264209, China

Mathematics, 2022, vol. 10, issue 11, 1-13

Abstract: With the increasing popularity of location-aware Internet-of-Vehicle services, the next-Point-of-Interest (POI) recommendation has gained significant research interest, predicting where drivers will go next from their sequential movements. Many researchers have focused on this problem and proposed solutions. Machine learning-based methods (matrix factorization, Markov chain, and factorizing personalized Markov chain) focus on a POI sequential transition. However, they do not recommend the user’s position for the next few hours. Neural network-based methods can model user mobility behavior by learning the representations of the sequence data in the high-dimensional space. However, they just consider the influence from the spatiotemporal dimension and ignore many important influences, such as duration time at a POI (Point of Interest) and the semantic tags of the POIs. In this paper, we propose a novel method called multi-dimension spatial–temporal recurrent neural networks (MST-RNN), which extends the ST-RNN and exploits the duration time dimension and semantic tag dimension of POIs in each layer of neural networks. Experiments on real-world vehicle movement data show that the proposed MST-RNN is effective and clearly outperforms the state-of-the-art methods.

Keywords: point of interest; recurrent neural networks; spatial–temporal prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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